Duplicate Detection Evaluation

Duplicate Detection Evaluation is a scholarly assessment framework that systematically measures the effectiveness, accuracy, and reliability of duplicate detection methods by defining specific metrics, benchmarks, and validation protocols to distinguish true duplicates from non-duplicates within a given dataset.

92

Publications

5.3K

Citations

263

Authors

113

Institutions

Publications per year

2017–2026

29

Authors

263

  1. FN
    Felix Naumann

    Hasso Plattner Institute, Germany

    6 publications · 195 citations

    6

    h-index

  2. KV
    Karin Verspoor

    The University of Melbourne, Australia

    5 publications · 156 citations

    5

    h-index

  3. JZ
    Justin Zobel

    RMIT University, Australia

    4 publications · 130 citations

    4

    h-index

  4. QC
    Qingyu Chen

    National Institutes of Health, United States

    4 publications · 130 citations

    4

    h-index

  5. UD
    Uwe Draisbach

    Hasso Plattner Institute, Germany

    4 publications · 118 citations

    4

    h-index

1–5 of 263

Venues

  1. Online Information Review

    3 publications · 332 citations

    3

    h-index

  2. Systematic Reviews

    3 publications · 432 citations

    3

    h-index

  3. PubMed

    Repository · 2 publications · 34 citations

    2

    h-index

  4. PLoS ONE

    2 publications · 128 citations

    2

    h-index

  5. 2

    h-index

1–5